As per documentation https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html : headerint, list of int, default ‘infer’ Row number(s) to use as the column names, and the start of the data. Default behavior is to infer the column names: if no names are passed the behavior is identical to header=0 and column names are inferred from the first line of the file, if column names are passed explicitly then the behavior is identical to header=None. Explicitly pass header=0 to be able to replace existing names. The header can be a list of integers that specify row locations for a multi-index on the columns e.g. [0,1,3]. Intervening rows that are not specified will be skipped (e.g. 2 in this example is skipped). Note that this parameter ignores commented lines and empty lines if skip_blank_lines=True, so header=0 denotes the first line of data rather than the first line of the file.

namesarray-like,可选 要使用的列名列表。如果文件包含标题行，则应该显式传递header=0以覆盖列名。此列表中的副本是不允许的。

columts = ['Day', 'PLMN', 'RNCname']
tempo = pd.read_csv("info.csv", sep=';', header=0, names=columts, index_col=False)

As per documentation https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html : headerint, list of int, default ‘infer’ Row number(s) to use as the column names, and the start of the data. Default behavior is to infer the column names: if no names are passed the behavior is identical to header=0 and column names are inferred from the first line of the file, if column names are passed explicitly then the behavior is identical to header=None. Explicitly pass header=0 to be able to replace existing names. The header can be a list of integers that specify row locations for a multi-index on the columns e.g. [0,1,3]. Intervening rows that are not specified will be skipped (e.g. 2 in this example is skipped). Note that this parameter ignores commented lines and empty lines if skip_blank_lines=True, so header=0 denotes the first line of data rather than the first line of the file.

namesarray-like,可选 要使用的列名列表。如果文件包含标题行，则应该显式传递header=0以覆盖列名。此列表中的副本是不允许的。

columts = ['Day', 'PLMN', 'RNCname']
tempo = pd.read_csv("info.csv", sep=';', header=0, names=columts, index_col=False)

确保指定pass header=None，并为第4列和第7列添加usecols=[3,6]。

你也可以用header=None调用read_table()(读取文件的第一行作为数据的第一行):

df = pd.read_table('test.tsv', sep=',', usecols=[3,6], header=None)

如果分隔符是\t(，则此函数更有用。TSV文件等)，因为默认分隔符是\t(不像read_csv的默认分隔符是，)。

为了读取一个没有标题的csv文件，并且只针对某些列，你需要为第4列和第7列传递params header=None和usecols=[3,6]:

df = pd.read_csv(file_path, header=None, usecols=[3,6])

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以前的答案是正确的，但在我看来，一个额外的names参数将使它完美，这应该是推荐的方式，特别是当csv没有头文件时。

解决方案

使用usecols和names参数

df = pd.read_csv(file_path, usecols=[3,6], names=['colA', 'colB'])

更多的阅读

或者使用header=None显式地告诉人们csv没有header(无论如何这两行是相同的)

df = pd.read_csv(file_path, usecols=[3,6], names=['colA', 'colB'], header=None)

这样你就可以通过

# with `names` parameter
df['colA']
df['colB'] 

而不是

# without `names` parameter
df[0]
df[1]

解释

基于read_csv，当显式传递名称时，header将表现为None而不是0，因此当存在名称时可以跳过header=None。